Results 231 to 240 of about 5,351 (266)

Hyperspectral Imagery Clustering With Neighborhood Constraints

IEEE Geoscience and Remote Sensing Letters, 2013
This letter presents a new technique for clustering hyperspectral images that exploits neighborhood-constrained spatial information. The main feature of the proposed method is the introduction of a neighborhood homogeneity index (NHI) and the use of this index to measure the spatial homogeneity in a local area.
Shanshan Li 0003, Bing Zhang 0001, An Li, Xiuping Jia, Lianru Gao, Man Peng   +5 more
openaire   +2 more sources

Sparsity-based classification of hyperspectral imagery

2010 IEEE International Geoscience and Remote Sensing Symposium, 2010
In this paper, a new sparsity-based classification algorithm for hyperspectral imagery is proposed. This algorithm is based on the concept that a pixel in hyperspectral imagery lies in a low-dimensional subspace and thus can be represented by a sparse linear combination of the training samples.
Yi Chen 0014, Nasser M. Nasrabadi, Trac D. Tran   +2 more
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Color Representation and Classification for Hyperspectral Imagery

2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
A hyperspectral image contains information in hundreds of spectral channels. The information is sparsely distributed in such a huge 3D image cube. In practical applications, it may be helpful if such a high dimensional data can be displayed into an informative color image.
Qian Du 0001, Nareenart Raksuntorn, Shangshu Cai, Nicolas H. Younan   +3 more
openaire   +2 more sources

Anomaly discrimination and classification for hyperspectral imagery

2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2015
Anomaly detection finds data samples whose signatures are spectrally distinct from their surrounding data samples. Unfortunately, it generally cannot discriminate its detected anomalies one from another. One common approach is to measure closeness of spectral characteristics among detected anomalies to determine if the detected anomalies are actually ...
Li-Chien Lee, Drew Paylor, Chein-I Chang
openaire   +2 more sources

Unified mixing model for hyperspectral imagery

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
Multiple mixing models for hyperspectral imagery have been developed over the years. The most common is the linear mixing model which states that each pixel's spectral signature is a linear combination of the unique materials (or endmembers) in the scene.
openaire   +1 more source

Mixed Noise Reduction in Hyperspectral Imagery

2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019
In this paper, a hyperspectral mixed noise reduction technique is proposed called HyMiR. We assume that hyperspectral images include Gaussian and sparse noise types. First, the Gaussian noise is removed using a recently-developed method called hyperspectral restoration (HyRes).
Behnood Rasti, Pedram Ghamisi, Jocelyn Chanussot   +2 more
openaire   +2 more sources

Constrained band selection for hyperspectral imagery

IEEE Transactions on Geoscience and Remote Sensing, 2006
Constrained energy minimization (CEM) has shown effective in hyperspectral target detection. It linearly constrains a desired target signature while minimizing interfering effects caused by other unknown signatures. This paper explores this idea for band selection and develops a new approach to band selection, referred to as constrained band selection (
Chein-I Chang, Su Wang 0002
openaire   +2 more sources

Toward the Vectorization of Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2023
Leyuan Fang, Yinglong Yan, Jun Yue 0004, Yue Deng 0001   +3 more
openaire   +1 more source

Atmospheric and topographic corrections for hyperspectral imagery

2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009
In mountainous areas, slope and altitude variations modulate the airborne sensed hyperspectral radiance image. A new algorithm, SIERRA, has been developed for atmospheric, relief and BRDF corrections in order to extract the surface reflectance in the form of bi-hemispherical albedo that does not depend on solar incidence and observation angles.
Véronique Achard, Xavier Lenot
openaire   +1 more source

Nonlinear mixture analysis for hyperspectral imagery

2009 IEEE International Geoscience and Remote Sensing Symposium, 2009
Nonlinear mixture analysis for hyperspectral imagery is investigated in this paper. A simple but effective nonlinear mixture model is adopted, where the multiplication of each pair of endmembers results in another “endmember”, representing nonlinear scattering effect during pixel construction process.
Nareenart Raksuntorn, Qian Du 0001
openaire   +2 more sources